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IEEE International Conference on Image Processing

作者： S. Shah J.K. Aggarwal J. Eledath J. Ghosh Computer and Vision Research Center Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA University of Texas at Austin Austin TX US Laboratory for Artificial Neural Systems Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

This paper presents a system for classification of scenes using a multisensor integration framework. Indoor scenes are imaged using a visual and an infrared sensor and the images processed in three stages to perform classification of sensed objects into two classes: human and background. Finally, information from individual classifiers is integrated in order to obtain an improved classification performance. Details of feature extraction and classification using neural network combining a multi-Bayesian framework are presented. Segmentation of the imaged scene is performed using existing techniques such as texture analysis and histogram modeling. Classification results on real-world data are presented. The system represents a first step in the development of improved, robust classifiers based on the concepts of neural networks and multisensor integration.

关键词： Layout Neural networks Infrared sensors Humans Feature extraction Image segmentation Image analysis Image texture analysis Performance analysis Histograms

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Motion vision sensor architecture with asynchronous self-signaling pixels

Motion vision sensor architecture with asynchronous self-sig...

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computer Architectures for Machine Perception (CAMP)

作者： M. Arias-Estrada D. Poussart M. Tremblay Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University QUE Canada HexaVision Technologies Inc. Sainte-Foy QUE Canada

A custom CMOS imager with integrated motion computation is described. The architecture is based on correlating in time moving edges. Edges are located in time by a custom sensor; and correlated in a coprocessing module. The sensor architecture is centered around a compact pixel with analog signal processing and digital self-signaling capabilities. The sensor pixels detect moving edges in the image and communicate their position using an address-event protocol associated to temporal stamps. The coprocessing module correlates the edges and computes the velocity vector map. The motion sensor could be used in applications such as self-guided vehicles, mobile robotics and smart surveillance systems. The article details the motion sensor architecture, the simulated performance, the VLSI implementation and some preliminary results on fabricated prototypes.

关键词： Image edge detection computer architecture Intelligent sensors CMOS image sensors Digital signal processing Image sensors Pixel Protocols Sensor systems and applications Intelligent vehicles

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Estimation of position and orientation from image sequence of a circle

Estimation of position and orientation from image sequence o...

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IEEE International Conference on robotics and Automation (ICRA)

作者： M. Sato J.K. Aggarwal Faculty of Engineering Tokyo Institute of Polytechnics Atsugi Kanagawa Japan Department of Electrical and Computer Engineering Computer and Vision Research Center University of Texas Austin Austin TX USA

A method to estimate the relative position and orientation of a camera to a circle having unknown radius is presented. A possible application of this problem is the automatic landing of a helicopter because the method can replace human pilots who estimate their position and orientation relative to the landing site by observing the circle marked in heliports. The method is formulated on the framework of the recursive estimation using an image sequence of a circle. The results of the experiment both on the synthetic data and the real image show the proposed method works adequately.

关键词： Image sequences Helicopters Cameras Humans State estimation Global Positioning System Land vehicles Geometry computer vision Application software

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REVIEW ■ : Prospects for a Visual Prosthesis

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The Neuroscientist 1997年第4期3卷 251 - 262页

作者： Joseph F. Rizzo John Wyatt Keck Neural Prosthesis Center Department of Ophthalmology Keck Neural Prosthesis Center Research Laboratory of Electronics Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts

Diseases of the retina and optic nerve are common causes of irreversible blindness. Given the lack of effective treatments, several laboratories are utilizing microelectronic technology to develop either a cortical or retinal prosthesis. Each strategy offers certain advantages, but both face numerous and formidable chal lenges. Consequently, a clinically useful device of either type is still conceptual. The technological means to build prostheses are available, but the ultimate obstacle is the integration of the technology with the brain. This article reviews achievements of the ongoing efforts and focuses on our project to develop a retinal prosthesis. NEUROSCIENTIST 3:251-262, 1997

关键词： KEY WORDS vision Prosthesis Retina Visual cortex Blindness electrical stimulation

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The FERET evaluation methodology for face-recognition algorithms

The FERET evaluation methodology for face-recognition algori...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： P.J. Phillips Hyeonjoon Moon P. Rauss S.A. Rizvi AMSlU-SE-SE US Army Research Laboratory Adelphi MD USA Department of Electrical and Computer Engineering State University of New York University at Buffalo Amherst NY USA Department of Applied Sciences University of New York Staten Island NY USA

Two of the most critical requirements in support of producing reliable face-recognition systems are a large database of facial images and a testing procedure to evaluate systems. The Face Recognition Technology (FERET) program has addressed both issues through the FERET database of facial images and the establishment of the FERET tests. To date, 14,126 images from 1199 individuals are included in the FERET database, which is divided into development and sequestered portions. In September 1996, the FERET program administered the third in a series of FERET face-recognition tests. The primary objectives of the third test were to (1) assess the state of the art, (2) identify future areas of research, and (3) measure algorithm performance on large databases.

关键词： Testing Image databases Face recognition Laboratories computer vision Image recognition Powders Milling machines Military computing Data engineering

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High-speed extraction of 3D structure of selectable quality using a translating camera

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computer vision AND IMAGE UNDERSTANDING 1996年第1期64卷 97-110页

作者： Dalmia, AK Trivedi, M Computer Vision and Robotics Research Laboratory Electrical and Computer Engineering Department The University of Tennessee Knoxville Tennessee 37996-2100

In this paper we present a computational approach for extracting three-dimensional structure of controllable resolution, depth of field, and accuracy, all made available at real-time speeds, This approach utilizes the spatial and the temporal gradients of the streams of images acquired using an actively controlled camera, Depending on the requirements of a particular task, appropriate parameters such as disparity value sought, interframe camera displacement, and number of frames in a stream are chosen to control the resolution, depth of field, and accuracy, The acquisition and processing of the image stream are done in real time on a pipeline architecture based processor. Extensive experiments are presented to demonstrate the system's accuracy, controllability of depth of field and resolution, and ability to perform successfully in a variety of scenes. The system operated with no latency between image acquisition and processing. The total acquisition and processing time in these experiments is in the range from 0.27 to 1.56 s. The depth results have an accuracy of 85% to 92%. (C) 1996 Academic Press, Inc.

关键词： computer vision

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Towards a general multi-view registration technique

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1996年第5期18卷 540-547页

作者： Bergevin, R Soucy, M Gagnon, H Laurendeau, D Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University QUE Canada

We present an algorithm that reduces significantly the level of the registration errors between all pairs in a set of range views. This algorithm refines initial estimates of the transformation matrices obtained from either the calibrated acquisition setup or a crude manual alignment. It is an instance of a category of registration algorithms known as iterated closest-point (ICP) algorithms. The algorithm considers the network of views as a whole and minimizes the registration errors of all views simultaneously. This leads to a well-balanced network of views in which the registration errors are equally distributed, an objective not met by previously published ICP algorithms which all process the views sequentially. Experimental results show that this refinement technique improves the calibrated registrations and the quality of the integrated model for complex multipart objects. In the case of scenes comprising man-made objects of very simple shapes, the basic algorithm faces problems common to all ICP algorithms and must thus be extended.

关键词： multiple view registration model building three-dimensional objects range images surface modeling multiple view integration

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A study of neural network control of robot manipulators

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ROBOTICA 1996年第1期14卷 7-15页

作者： Jung, S Hsia, TC [a1 ]Robotics Research Laboratory Department of Electrical and Computer Engineering University of California at Davis Davis CA 95615 (USA)

The basic robot control technique is the model based computer-torque control which is known to suffer performance degradation due to model uncertainties. Adding a neural network (NN) controller in the control system is one effective way to compensate for the ill effects of these uncertainties. In this paper a systematic study of NN controller for a robot manipulator under a unified computed-torque control framework is presented. Both feedforward and feedback NN control schemes are studied and compared using a common back-propagation training algorithm. Effects on system performance for different choices of NN input types, hidden neurons, weight update rates, and initial weight values are also investigated. Extensive simulation studies for trajectory tracking are carried out and compared with other established robot control schemes.

关键词： neural networks simulation studies robot control

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Designing stereo heads using task domain constraints

Designing stereo heads using task domain constraints

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13th International Conference on Pattern Recognition, ICPR 1996

作者： Thayer, Scott M. Gourley, Christopher S. Electrical and Computer Engineering Department Computer Vision and Robotics Research Laboratory University of Tennessee Knoxville TN 37996-2100 United States

ISBN: (纸本)081867282X

A novel paradigm for the synthesis of convergent-axis stereo geometries is presented. This paradigm incorporates constraints that represent task-oriented properties that can be easily derived in many applications of stereo-ranging, particularly those involving manipulation. Given these properties, a solution that selects the set of optimal design parameters, in the sense of accuracy, under the given constraints is presented. Results of this algorithm are discussed and analyzed in terms of the optimality of the minimum-area technique used in the derivation. © 1996 IEEE.

关键词： Pattern recognition

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3D free-form surface registration and object recognition

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INTERNATIONAL JOURNAL OF computer vision 1996年第1期17卷 77-99页

作者： Chua, CS Jarvis, R Intelligent Robotics Research Center Department of Electrical and Computer Systems Engineering Monash University Wellington Road 3168 Clayton Vic Australia

A new technique to recognise 3D free-form objects via registration is proposed. This technique attempts to register a free-form surface, represented by a set of 2 1/2D sensed data points, to the model surface, represented by another set of 2 1/2D model data points, without prior knowledge of correspondence or vie;tv points between the two point sets. With an initial assumption that the sensed surface be part of a more complete model surface, the algorithm begins by selecting three dispersed, reliable points on the sensed surface. To find the three corresponding model points, the method uses the principal curvatures and the Darboux frames to restrict the search over the model space. Invariably, many possible model 3-tuples will be found. For each hypothesized model 3-tuple, the transformation to match the sensed 3-tuple to the model 3 tuple can be determined. A heuristic search is proposed to single out the optimal transformation in low order time. For realistic object recognition or registration, where the two range images are often extracted from different view points of the model, the earlier assumption that the sensed surface be part of a more complete model surface cannot be relied on. With this, the sensed 3-tuple must be chosen such that the three sensed points lie on the common region visible to both the sensed and model views. We propose an algorithm to select a minimal non-redundant set of 3-tuples such that at least one of the S-tuples will lie on the overlap. Applying the previous algorithm to each 3-tuple within this set, the optimal transformation can be determined. Experiments using data obtained from a range finder have indicated fast registration for relatively complex test cases. If the optimal registrations between the sensed data (candidate) and each of a set of model data are found, then, for 3D object recognition purposes, the minimal best fit error can be used as the decision rule.

关键词： computer vision

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